1. Field of the Invention
The present invention relates to a system and method for the analysis of respiratory-cycle related electroencephalogram (EEG) changes in sleep-disordered breathing.
2. Background Art
Obstructive sleep-disordered breathing (SDB) in children includes obstructive sleep apnea, upper airway resistance syndrome, and obstructive hypoventilation. In obstructive sleep apnea, repeated airflow cessation (apnea) or decrement (hypopnea) leads to arousal, oxygen desaturation, and sleep fragmentation. In upper airway resistance syndrome, repeated partial closure of the upper airway leads to sequences of increasing effort to breathe, terminated by brief arousals in the absence of significant hypoxemia. In obstructive hypoventilation, a narrowed airway leads to steady but increased effort to breathe and hypercarbia, without visible arousals. The most frequent cause of SDB in children is adenotonsillar hypertrophy, and adenotonsillectomy in these children is thought to be a highly effective intervention. Causes in adults may include obesity, genetic contributions, and neurophysiological factors.
Several studies suggest that the prevalence of obstructive sleep apnea among children is 1 to 3%, whereas the prevalence of habitual snoring, with or without obstructive SDB, is 5 to 12%. The prevalence of upper airway resistance syndrome and obstructive hypoventilation are unknown, but these disorders could be present in a substantial portion of children with habitual snoring. Severe childhood SDB can lead to failure to thrive, growth retardation, cor pulmonale, and systemic hypertension. Unrecognized or incompletely treated SDB may contribute to behavioral and cognitive problems, including inattentive and hyperactive behavior, aggressive conduct, and daytime sleepiness.
As childhood SDB is common and treatable, identification of the condition is important, preferably before consequences develop. However, most children with SDB may not be identified. Epidemiological data from adults show that >90% of women and >80% of men with obstructive sleep apnea are not diagnosed. Among children, SDB was undiagnosed in 92% of affected children who participated in one community-based research study.
Unfortunately, the mechanism by which sleepiness and other cognitive changes are produced in SDB is not fully understood, and the optimal approach to the identification of children with SDB is controversial. Most attention has been focused on the role of arousals, which typically occur at the termination of each apnea, hypopnea, or transient period of increased respiratory effort. Arousals from sleep are believed to contribute substantially to SDB morbidity, and cortical EEG signals obtained via polysomnography have been used to define sleep and wakefulness for many years. In the assessment of patients for SDB, current state-of-the-art methodology focuses largely on the apnea/hypopnea index: the number of times per hour of sleep that a patient has absence of breathing (apnea), diminished breathing (hypopnea), or difficult breathing (respiratory event-related arousal).
To determine the apnea/hypopnea index, patients undergo polysomnographic recordings wherein technicians manually classify sleep stages and manually count episodes of apneas, hypopneas, and sometimes the more subtle respiratory event-related arousals. Manual scoring of apneic events recorded on sleep studies is time-consuming, expensive, subject to inter-scorer variability, and based on definitions that can vary considerably between laboratories. Software that automates the process has been available for years, but generally is not considered to be an adequate substitute. The main result of manual apnea scoring—the rate of events per hour of sleep—only poorly predicts sleepiness, inattention, hyperactivity, and outcomes of sleep-disordered breathing in children and adults. For example, randomized controlled trials confirm that SDB causes excessive daytime sleepiness, but the rate of apneas and hypopneas—often used as the best single polysomnographic measure of SDB severity—only predicts about 11% of the variance in objectively measured sleepiness.
One possible explanation is that subtle but clinically-relevant SDB is often missed, as features of SDB that most affect outcomes may not be recorded or analyzed. In particular, many patients, and especially children, do not always show visually-recognizable EEG arousals after apneic events (see McNamara et al., J Appl Physiol 1996, 81:2651–7). In practice, arousals are most often defined primarily by transient increases in EEG frequency. More subtle arousals, as reflected by autonomic changes in the absence of visible cortical EEG activation, can also cause sleepiness when experimentally induced (see Douglas and Martin, Sleep 1996, 19:S196–7), but may not correlate any better with sleepiness in non-experimental settings. Computerized approaches to this problem have focused entirely on apneic events that appear themselves to be inadequate predictors of outcomes.
Clearly, associations between apnea/hypopnea indices and subjective sleepiness can be difficult to detect. In the largest samples, subjects with the most frequent apneic events, in comparison to those with the least, score only 2 points higher on a 24-point subjective sleepiness scale. In addition, snoring predicts sleepiness even after accounting for the rate of apneas and hypopneas. Among children, surveys about snoring and other clinical signs of SDB repeatedly show robust correlations with behavioral morbidity, but data from polysomnographic studies of SDB show less consistent associations. These findings parallel those from adults, among whom polysomnographic measures tend to explain only small portions of measured sleepiness, and a history of snoring still predicts sleepiness after objective measures are taken into account.
A desire to improve the ability of polysomnograms to predict SDB outcomes has given rise to new equipment and strategies to monitor breathing or breathing effort, such as esophageal pressure monitoring (see Guilleminault et al., Chest 1993, 104:781–7), nasal pressure monitoring (see Ayappa et al., Sleep 2000, 23:763–71), and the forced oscillation technique (see Navajas et al., Am J Resp Crit Care Med 1998, 157:1526–30). Other approaches have focused on non-visible EEG changes detected by signal analysis (see Black et al., Am J Crit Care Med 2000, 162:406–11; Bandla and Gozal, Pediatr Pulmonol 2000, 29:359–65; Dingli et al., Eur Respir J 2002, 20:1246–53), body movements (see Bennett et al., Am J Resp Crit Care Med 1998, 158:778–86), or subtle autonomic changes (see Pitson and Stradling, J Sleep Res 1998, 7:53–9; Stradling et al., J Sleep Res 2000, 9:381–8). However, none of these methods, all linked to the concept that discrete apneic events in some way cause sleepiness, have substantially improved prediction of important SDB outcomes.
Therefore, physiologically important features of SDB or sleepiness may not be adequately assessed by current methods. The problem may be particularly relevant to children, whose apneic events can be short, subtle, unaccompanied by visible arousal, and difficult to detect.